Channel attention concatenation multi-taper Fbank features for deep speaker verification

Automatic speaker verification (ASV) has become a widely used application of deep learning. However, many early positive findings were rooted in traditional methods like Gaussian mixture models (GMM) rather than deep learning. While the multi-taper spectrum estimator has proven effective in enhancing GMM-based ASV accuracy, the integration of traditional multi-tapers with modern deep learning models may not be seamless. To address this, we introduce the Channel Attention Concatenation Multi-taper Fbank (CCM-Fbank), which seamlessly integrates multi-taper spectral estimation with the popular ECAPA-TDNN model, resulting in improved accuracy and robustness. Additionally, we propose a deeper model named Double Block ECAPA-TDNN, which has just over half the number of parameters of ECAPA-TDNN (C=1024), and performs better with limited training samples.